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Yuan, J.

Publications and source records attributed to Yuan, J..

At least 55 records · Page 3Linked to original sources

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

biophysics↗

Endothelial Kallikrein-Related Peptidase 8 Promotes Diabetic Nephropathy through a LIFR dependent mechanism

BackgroundDiabetic nephropathy (DN) is the primary microvascular complication of diabetes mellitus; however, the exact pathways in endothelial cells (ECs) linked to DN progress remain unclear. Tissue kallikrein-related peptidases (KLKs) participate in pathophysiological processes in ECs. We aimed to explore the roles of endothelial KLKs in DN and define the underlying mechanisms. Methods and resultsKLK8 was the most highly upregulated member of KLKs in renal tissues in streptozotocin (STZ)-induced diabetic mice and cultured glomerular ECs (GECs) upon high glucose (HG) treatment. Both global (Klk8-/-) and endothelial Klk8 knockout (Klk8{Delta}EC) mice displayed improved albuminuria, mesangial matrix expansion and glomerulosclerosis caused by STZ compared to Klk8f/f mice. Single-cell RNA seq (scRNA-seq) showed that many pathways associated with DN in ECs, mesangial cells (MCs) and tubule cells were reversed in Klk8{Delta}EC mice. Endothelial-to-mesenchymal transition (EndMT) was extensively improved in Klk8{Delta}EC mice, and by KLK8 siRNA in cultured GECs upon HG. Using proteome and other biochemical approaches, we revealed that KLK8 cleaved syndecan-4(SDC4), which contributed to loss of glycocalyx integrity in GECs in cultured cells and animal diabetic models. Furthermore, scRNA-seq showed that Lifr was one of the key genes linked to the disease progressed in ECs and MCs and regulated by endothelial Klk8. LIFR signaling contributed to HG-induced GEC dysfunction and MC activation, which was associated with endothelial Klk8. Knockdown of Lifr by lentivirus-Lifr shRNA ameliorated hallmark features of DN and improved EndMT and Sdc4 expression glomeruli of diabetic mice. LIFR was upregulated in GECs and MCs in DN patients. Circulatory levels of LIF, KLK8 and soluble SDC4 were increased in patients with DN, and KLK8 level was positively correlated with LIF, soluble SDC4 and creatinine levels. ConclusionEndothelial KLK8 promotes GECs dysfunction and abnormal crosstalk with MCs through a LIFR-dependent signaling in DN, which immediately highlights therapeutic targets for DN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=171 SRC="FIGDIR/small/662673v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@dae3b1org.highwire.dtl.DTLVardef@9aab24org.highwire.dtl.DTLVardef@59eaa5org.highwire.dtl.DTLVardef@13c138a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

Single-Cell and Spatial Profiling of Tumor Microenvironment Heterogeneity in Human Osteosarcomas

Osteosarcoma, a highly heterogeneous malignant bone tumor, exhibits substantial interpatient and intratumoral heterogeneity. Utilizing integrated single-cell RNA sequencing and spatial transcriptomics, we uncovered distinct tumor microenvironment (TME) transcriptional profiles across patients, highlighting profound interpatient heterogeneity. Intratumorally, malignant cells primarily followed two differentiation trajectories, converging into osteoblastic and chondroblastic functional states, and hypoxia may be the influencer; notably, within individual patients, tumor cells exhibited greater transcriptional similarity driven by specific transcription factors, despite these divergent states. Spatial analysis revealed patient-specific TME cellular co-localization patterns, alongside conserved spatial relationships: vascular components (endothelial cells, pericytes) demonstrated strong co-localization, while immune cells (T cells, myeloid cells) clustered within shared regions. Crucially, these functional states occupied distinct microniches: osteoblastic regions were enriched with osteoclasts, vascular components, and immune cells, whereas chondroblastic regions displayed the opposite composition and were preferentially located in hypoxic, vascular-poor niches, exhibiting significant enrichment of hypoxia-related signaling pathways. Furthermore, our data suggest osteosarcoma cells may activate fibroblasts via the SPP1 signaling pathway, indicating fibroblasts act as key intermediaries in tumor-directed TME modulation. This study comprehensively delineates the intricate landscape of osteosarcoma heterogeneity, defining distinct functional cellular states, their spatially organized TME niches, and a potential SPP1-mediated tumor-fibroblast regulatory axis. This comprehensive analysis elucidates the intricate interpatient and intratumoral heterogeneity in osteosarcoma, revealing functional and spatial organization within the tumor and its microenvironment.

cancer biology↗

In Vivo Tensor-Valued Diffusion MRI Reveals Isotropic-Anisotropic Kurtosis Mismatch in a Rat MCAO Stroke Model

BackgroundTensor-valued diffusion MRI (dMRI) enables the separation of total kurtosis into isotropic and anisotropic components, offering improved specificity over conventional Diffusion Kurtosis Imaging (DKI). In this study, we introduce a novel framework for detecting anisotropic-isotropic kurtosis mismatch and evaluate its utility in characterizing stroke lesions. MethodsWe performed tensor-valued dMRI in a rat model of middle cerebral artery occlusion (MCAO). Metrics including anisotropic kurtosis (MKA) and isotropic kurtosis (MKI) were quantified to assess microstructural tissue heterogeneity. Histological validation was conducted using co-registered tissue sections. ResultsWe observed significant mismatches between mean diffusivity (MD), mean kurtosis (MK), as well as specifically in MKA and MKI in ischemic regions. Notably, MKA and MKI exhibited statistically significant alterations compared to contralateral regions. Moreover, these metrics strongly correlated with pathological findings. The mismatch highlighted microstructurally distinct subregions within the lesion. ConclusionsThis is the first study to propose an anisotropic-isotropic kurtosis mismatch framework in vivo. Tensor-valued dMRI may offer a more specific imaging biomarker for stroke, with potential for improving lesion subtyping and guiding therapeutic decisions.

biophysics↗

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing

Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome-wide coverage of only 0.1-5x, sWGS data exhibit a pronounced zero-inflation phenomenon, whereby a large fraction of loci contains zero sequencing reads. Zero-inflation causes read counts to fluctuate by several-fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy-number gains (false positives), and true deletions often become indistinguishable from pervasive zero-coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a large sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

bioinformatics↗

cfDNAanalyzer: a comprehensive toolkit for analyzing cell-free DNA genomic sequencing data in liquid biopsy

Liquid biopsy, through the analysis of plasma cell-free DNA (cfDNA), is transforming diagnostic medicine by enabling non-invasive insights into the genomic and epigenomic landscape of diseases--most notably cancer. As cfDNA reflects dynamic contributions from multiple tissues, it holds immense potential for early detection, disease monitoring, and personalized treatment. However, the rapid expansion of cfDNA applications has outpaced the availability of unified, versatile computational tools for the systematic analysis of cfDNA data. To address this unmet need, we present cfDNAanalyzer--a comprehensive, user-friendly toolkit that streamlines the feature extraction, selection, and construction of machine learning-based models for disease detection using cfDNA. Designed for flexibility and scalability, cfDNAanalyzer supports multimodal feature extraction, integration, and interpretable outputs, enabling both exploratory research and translational applications. By standardizing and accelerating cfDNA data analysis, cfDNAanalyzer provides a critical resource to advance precision diagnostics and deepen biological insight into disease processes.

bioinformatics↗

The lac repressor strikes a balance between inducibility and strength of repression

Transcription factors (TFs) efficiently locate their target DNA sequences by combining three-dimensional diffusion and one-dimensional sliding on nonspecific DNA. To balance rapid sliding with strong specific binding, TFs were proposed to switch between search and recognition conformations. For E. coli lac repressor (LacI), the folding of the hinge helices has been implicated in the conformational switch. Here, we tested how mutations in the hinge region impact the search speed and binding stability. Based on molecular dynamics simulations, we selected two LacI mutants favoring either search or recognition conformation. We measured the binding kinetics of the mutants both in vitro on DNA microarrays with 2,479 different Lac operators and in vivo via single-molecule experiments. We conclude that a hinge region mutation causing less helix propensity enhances the specificity but reduces binding strength globally, while a hinge region mutation causing higher helix propensity has opposite effects. However, altered specificity impacts the search time less than expected. Instead, the major effect was impaired dissociation in response to IPTG induction for the strongly binding mutant. Together with earlier reports of affinity-inducibility trade-offs in LacI, our data support the model in which the hinge region governs a trade-off between binding stability and inducibility rather than between speed and binding stability.

molecular biology↗

EpiMII: Integrating Structure and Graph Neural Networks for MHC-II Epitope and Neoantigen Design

MHC-II neoantigens play a critical role in immunotherapy, either as direct effectors or through their influence on CD8+ T cells. However, only a small fraction of tumor DNA mutations qualify as functional neoantigens, and current prediction tools often lack accuracy, leading to the low immunogenicity of predicted neoantigens in vivo. Here, we present EpiMII, a Graph Neural Network model for MHC-II epitope design, which learns from the structural features of epitopes to predict their sequences. To train EpiMII, we constructed a reliable, large dataset containing 142,934 MHC-II epitope structures. This approach achieves a 4.2x improvement over ProteinMPNN, with a sequence recovery rate of 78.0% for known MHC-II epitopes in the Protein Data Bank. As a case study, we designed a neoantigen from hepatocellular carcinoma. All five designed epitopes significantly activated CD4+ T cells in vitro and induced secretion of IFN-{gamma} and TNF-. Notably, one epitope treatment significantly reduced tumor volume in mice in vivo. EpiMII offers a novel and efficient approach for identifying MHC-II epitopes/neoantigens, potentially contributing to vaccine development.

immunology↗

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Transcription initiation is a critical regulatory step in plant gene expression, yet its sequence determinants remain largely elusive. Here we introduce GenoRetriever, an interpretable deep learning model that deciphers the sequence basis of transcriptional initiation regulation across plant genomes. Trained on STRIPE-seq data from 16 soybean tissues and six other crop species, GenoRetriever identifies 27 core sequence motifs that govern transcription start site (TSS) selection and usage. The model predicts TSS locations and usage levels with high accuracy, as validated by in silico motif insertions, saturation mutagenesis, and CRISPR-Cas9 promoter editing. It further reveals that 31.85% of natural variation between wild and domesticated soybean drives shifts in promoter motif usage during domestication, and uncovers lineage-specific motif effects between monocots and dicots. This interpretable model and its user-friendly web server for promoter analysis and design make GenoRetriever both a methodological innovation and practical tool for plant functional genomics and crop improvement.

plant biology↗

STmiR: A Novel XGBoost-Based Framework for Spatially Resolved miRNA Activity Prediction

MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology; however, their spatial dynamics within tumor microenvironments (TME) remain underexplored owing to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity predictions. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving a high predictive accuracy (Spearmans {rho} > 0.8) across four major cancer types (breast, lung, ovarian, and prostate). Applied to 10X Visium ST datasets from nine cancers, STmiR identified six pan-cancer conserved miRNAs (e.g., hsa-miR-21, hsa-let-7a) consistently ranked in the top 40 across malignancies, and uncovered cell-type-specific regulatory networks in fibroblasts, B cells, and malignant cells. A breast cancer case study validated the utility of STmiR by linking miR-205 to androgen receptor (AR) signaling and miR-200b to epithelial-mesenchymal transition (EMT). By enabling the spatial mapping of miRNA activity, STmiR provides a transformative tool to dissect miRNA-mediated regulatory mechanisms in cancer progression and TME remodeling, with implications for biomarker discovery and precision oncology. HighlightsO_LIFirst integration of XGBoost and spatial transcriptomics: STmiR pioneers a machine learning framework to predict miRNA activity in spatially heterogeneous tissues, overcoming limitations of linear correlation-based methods. C_LIO_LIHigh accuracy and generalizability: Demonstrates robust performance (Spearmans {rho} > 0.8) across four cancer types validated by independent datasets. C_LIO_LIPan-cancer conserved miRNAs: Six miRNAs (e.g., hsa-miR-21 and hsa-let-7a) were shared across nine cancers, implicating their roles in core oncogenic pathways. C_LI Key ContributionsO_LIMethodological innovation: Combines XGBoosts nonlinear modeling with spatial transcriptomics to resolve miRNA activity in multicellular contexts. C_LIO_LIBiological discovery: Uncovers the conserved and context-dependent roles of miRNAs in tumor progression and microenvironment crosstalk. C_LIO_LITranslational impact: Provides a computational platform for identifying spatially regulated therapeutic targets in cancer. C_LI

bioinformatics↗

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

Store-operated Ca2+ entry is involved in endothelium-to-mesenchymal transition in lung vascular endothelial cells

Endothelial-to-mesenchymal transition (EndMT) is a biological process that converts endothelial cells to mesenchymal cells with increased proliferative and migrative abilities. EndMT has been implicated in the development of pulmonary vascular remodeling in pulmonary arterial hypertension (PAH), a fatal and progressive lung vascular disease. Transforming growth factor {beta}1 (TGF-{beta}1), an inflammatory cytokine, is known to induce EndMT in many types of endothelial cells including lung vascular endothelial cells (LVEC). An increase in cytosolic free Ca2+ concentration ([Ca2+]cyt) is a major stimulus for cellular proliferation and phenotypic transition, but it is unknown whether Ca2+ signaling is involved in EndMT. In this study we tested the hypothesis that TGF-{beta}1-induced EndMT in human LVEC is Ca2+-dependent. Treatment of LVEC with TGF-{beta}1 for 5-7 days resulted in increase in SNAI1/2 expression, induction of EndMT, upregulation of STIM/Orai1 and enhancement of store-operated Ca2+ entry (SOCE). Removal (or chelation) of extracellular or intracellular Ca2+ with EGTA or BAPTA-AM respectively abolished EndMT in response to TGF-{beta}1. Moreover, EGTA diminished TGF-{beta}1-induced increase in SNAI in a dose-dependent manner. Knockdown of either STIM1 or Orai1 was sufficient to prevent TGF-{beta}-mediated increase in SNAI1/2 and EndMT, but did not rescue the continuous adherent junctions. Blockade of Orai1 channels by AnCoA4 inhibited TGF-{beta}-mediated EndMT and restored PECAM1-positive continuous adherent junctions. In conclusion, intracellular Ca2+ signaling plays a critical role in TGF-{beta}-associated EndMT through enhanced SOCE and STIM1-Orai1 interaction. Thus, targeting Ca2+ signaling pathways regulating EndMT may be a novel therapeutic approach to treat PAH and other forms of pre-capillary pulmonary hypertension. New & NoteworthyEndMT has been reported to contribute to the pathogenesis of PH. In this study we aimed to determine the role of Ca2+ signaling in the development of EndMT in human lung vascular endothelial cells. Our data suggest that TGF-{beta}1 requires store-operated Ca2+ entry through STIM1/Orai channels to induce SNAI-mediated EndMT. For the first time we demonstrated that TGF-{beta}1-induced EndMT is Ca2+-dependent event while inhibition of STIM1/Orai interaction attenuated EndMT in response to TGF-{beta}1.

cell biology↗

HIF2α negatively regulates MYCN protein levels and promotes a low-risk noradrenergic phenotype in neuroblastoma.

The role of HIF2, encoded by EPAS1, in neuroblastoma remains controversial. Here we demonstrate that induction of high levels of HIF2 in MYCN-amplified neuroblastoma cells results in a rapid and profound reduction of the oncoprotein MYCN. This is followed by an upregulation of genes characteristic of noradrenergic cells in the adrenal medulla. Additionally, upon induction of HIF2, the proliferation rate drops substantially, and cells develop elongated neurite-like protrusions, indicative of differentiation. In vivo HIF2 induction in established xenografts significantly attenuates tumour growth. Notably, analysis of sequenced neuroblastoma patient samples, revealed a negative correlation between EPAS1 and MYCN expression and a strong positive correlation between EPAS1 expression, high expression levels of noradrenergic markers, and improved patient outcome. This was paralleled by analysis of human adrenal medulla datasets wherein EPAS1 expression was prominent in populations with high expression levels of genes characteristic of noradrenergic chromaffin cells. Our findings show that high levels of HIF2 in neuroblastoma, leads to drastically reduced MYCN protein levels, cell cycle exit, and noradrenergic cell differentiation. Taken together, our results challenge the dogma that HIF2 acts as an oncogene in neuroblastoma and rather suggest that HIF2 has potential tumour suppressor capacity in this particular disease. Significance statementHIF2 has been proposed as a neuroblastoma oncogene and a tractable target for clinical intervention, this has been questioned by several studies. Thus, it is necessary to move beyond correlative studies and better determine the function of HIF2 in neuroblastoma. Our study shows that induced expression of HIF2 in MYCN-amplified neuroblastoma substantially reduces MYCN protein levels and attenuates proliferation while it induces several features of noradrenergic differentiation and impedes xenograft tumour growth. Together with bioinformatic analysis of sequenced neuroblastoma patient samples and the developing human adrenal medulla, this couples HIF2 to low-risk neuroblastoma with a substantially better patient outcome. Thus, in neuroblastoma HIF2 exhibit tumour suppressor capacity rather than oncogenic capacity.

cancer biology↗

Volumetric imaging and computation to explore contractile function in zebrafish hearts

Despite advancements in cardiovascular engineering, heart diseases remain a leading cause of mortality. The limited understanding of the underlying mechanisms of cardiac dysfunction at the cellular level restricts the development of effective screening and therapeutic methods. To address this, we have developed a framework that incorporates light field detection and individual cell tracking to capture real-time volumetric data in zebrafish hearts, which share structural and electrical similarities with the human heart and generate 120 to 180 beats per minute. Our results indicate that the in-house system achieves an acquisition speed of 200 volumes per second, with resolutions of up to 5.02 {+/-} 0.54 {micro}m laterally and 9.02 {+/-} 1.11 {micro}m axially across the entire depth, using the estimated-maximized-smoothed deconvolution method. The subsequent deep learning-based cell trackers enable further investigation of contractile dynamics, including cellular displacement and velocity, followed by volumetric tracking of specific cells of interest from end-systole to end-diastole in an interactive environment. Collectively, our strategy facilitates real-time volumetric imaging and assessment of contractile dynamics across the entire ventricle at the cellular resolution over multiple cycles, providing significant potential for exploring intercellular interactions in both health and disease.

bioengineering↗

PhenoScreen: A Dual-Space Contrastive Learning Framework-based Phenotypic Screening Method by Linking Chemical Perturbations to Cellular Morphology

Phenotypic drug discovery (PDD) screens compounds in cellular models that represent disease-relevant phenotypes, offering a compelling alternative to traditional target-based approaches. Unlike conventional methods, where compounds act on a single predefined target, PDD identifies compounds capable of exerting therapeutic effects through multiple targets and mechanisms. This makes PDD particularly valuable for discovering first-in-class drugs, especially for diseases with poorly understood molecular mechanisms or those lacking validated therapeutic targets. By enabling broader exploration of biological systems and uncovering multi-target drugs (polypharmacology), PDD provides a powerful strategy for tackling complex diseases. In this study, we introduce PhenoScreen, a deep learning framework designed to advance PDD by utilizing large-scale compound-phenotype association data. Through contrastive learning, PhenoScreen connects chemical space with cellular morphological profiles, allowing for accurate prediction of compound-induced phenotypic changes. PhenoScreen can also accurately identfiy lead compounds which could induce user defined phenotypic shift but more novel scaffolds using different levels of phenotypic information reflected by diverse compounds. The model was validated across multiple screening tasks and successfully predicted active compounds inducing user-specified phenotypes with varying inhibitory effects in the osteosarcoma phenotypic model. Further, other than showing effectiveness to osteosarcoma, our experiments also showed that PhenoScreen demonstrated strong generalization to rhabdomyosarcoma, and the active compound we screened had an IC50 of up to 1.842 M, suggesting its ability to capture key phenotypic features shared across cancer cells. These results underscore PhenoScreens potential to accelerate drug discovery by identifying novel therapeutic pathways and increasing the diversity of viable drug candidates. PhenoScreen is accessible online via our groups web server for compound virtual screening at https://bailab.siais.shanghaitech.edu.cn/services/PhenoScreen/, and the source codes are available at https://github.com/Shihang-Wang-58/PhenoScreen.

bioinformatics↗

Connectomic Organization of the Suprachiasmatic Nucleus

The suprachiasmatic nucleus (SCN), the mammalian master clock, is a special structure dedicated to the time computation. However, a connectomic understanding of the nucleus as a whole is lacking. Using serial section electron microscopy, we reconstructed multiscale and multimodal SCN communication networks. Intra-SCN synaptic network consists of 9,566 morphologically similar neurons and 4.3 million synapses, organized into multiscale circuitries that interact with SCN-traversing axon fascicles. Strikingly, SCN neurons engage in unique soma-soma ephaptic interaction, forming 2,038 electrotonic integrative units with the largest overlapping the light-responsive area (LRA). SCNs paracrine modality contains 47,396 m3 dense core vesicles, with notable scarcity in the LRA, suggesting cross-modal coordination and functional integration. These distinct network features provide comprehensive insights into the system architecture of the mammalian circadian clock.

neuroscience↗

Gene encoding CC-NBS-LRR protein on rye chromosome 1RS confers wheat stripe rust resistance

Stripe rust, a globally widespread disease, stands as one of the most significant threats to wheat cultivation. The 1BL/1RS translocation, renowned for its robust resistance to both rust and powdery mildew, has historically played an important role in wheat breeding and production. The gene for resistance to stripe rust on the 1RS is known as Yr9 and plays an important role in the production of wheat, but over the course of long-term breeding had lost its resistance due to the evolution of stripe rust towards greater and greater virulence. In this paper, we cloned the stripe rust resistance gene, Yr9, from triticale by genetic mapping approach. The Yr9 encodes a typical nucleotide-binding leucine-rich repeat (NLR) protein. Both transgenic and overexpression of Yr9 in highly stripe rust susceptible wheat varieties conferred complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34. In addition, the Yr9 allele in the 1BL/1RS translocation line also showed the same level of resistance to stripe rust. Both two alleles loses resistance when deployed in the field or inoculated with mixed physiological races collected from the field. Our findings provide valuable insights for breeders to strategically incorporate disease resistance genes and provides a foundation for further understanding how pathogenic bacteria might evolve to evade recognition via NLR type proteins. SignificanceThe 1BL/1RS translocation between wheat and rye is the most successful case of exogenous gene application in plant genetic improvement and has been used in wheat breeding for over 50 years. Here we report the cloning of a stripe rust resistance gene Yr9 located on rye chromosome 1RS using a triticale population. The Yr9 encodes a coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) protein that show complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34, albeit demonstrating susceptibility under field conditions. Our findings position Yr9 as an ideal candidate gene to study the mechanism of inactivation of disease resistance genes as a result of pathogen evolution.

genetics↗

Elevated NAD+ drives Sir2A-mediated GCβ deacetylation and OES localization for Plasmodium ookinete gliding and mosquito infection

cGMP signal-activated ookinete gliding is essential for mosquito midgut infection of Plasmodium in malaria transmission. During ookinete development, cGMP synthesizer GC{beta} polarizes to a unique localization <ookinete extrados site= (OES) until ookinete maturation and activates cGMP signaling for initiating parasite motility. However, the mechanism underlying GC{beta} translocation from cytosol to OES remains elusive. Here, we used protein proximity labeling to search the GC{beta}-interacting proteins in ookinetes of the rodent malaria parasite P. yoelii, and found the top hit Sir2A, a NAD+-dependent sirtuin family deacetylase. Sir2A interacts with GC{beta} throughout ookinete development. In mature ookinetes, Sir2A co-localizes with GC{beta} at OES in a mutually dependent manner. Parasites lacking Sir2A lose GC{beta} localization at OES, ookinete gliding, and mosquito infection, phenocopying GC{beta} deficiency. GC{beta} is acetylated at gametocytes but is deacetylated by Sir2A for OES localization at mature ookinetes. We further demonstrated that the level of NAD+, an essential co-substrate for sirtuin, increases during the ookinete development. The NAD+ at its maximal level until ookinete maturation promotes Sir2A-catalyzed GC{beta} deacetylation, ensuring GC{beta} localization at OES. This study highlights the spatiotemporal coordination of cytosolic NAD+ level and NAD+-dependent Sir2A in regulating GC{beta} deacetylation and dynamic localization for Plasmodium ookinete gliding.

microbiology↗